Defect detection methods and devices for wafers

Abstract

This invention provides a method and apparatus for detecting defects in wafers, belonging to the field of semiconductor manufacturing technology. The defect detection method includes: acquiring defect data from the surface of each wafer, the defect data including the type, size, and location coordinates of the defects; dividing the wafers in the same batch into uniformly defined regions, each wafer being divided into I regions, calculating the defect density value of each region, the defect density value of the i-th region being determined by the defect data of the i-th region and the area of ​​the i-th region, the defect data of the i-th region being the sum of the defect data of all wafers in the same batch in the i-th region; comparing the defect density values ​​of each region with a defect density threshold; if the defect density value of a region is more than N times the corresponding defect density threshold, or if the defect density value of the same region of more than M wafers in the batch is greater than the corresponding defect density threshold, the wafers in that batch are determined to be abnormal wafers. This invention can detect batch defects in wafers.

Description

Technical Field

[0001] This invention relates to the field of semiconductor manufacturing technology, and in particular to a method and apparatus for detecting defects in wafers. Background Technology

[0002] With the continuous advancement of semiconductor manufacturing technology, defect control on wafer surfaces has become increasingly important. In actual production, wafer defect distribution is influenced by various factors, such as front-end process fluctuations, material quality variations, and equipment maintenance. These influences can lead to specific defect distribution characteristics within the same batch of wafers. Existing wafer inspection methods mostly focus on defect density analysis of individual wafers. For some defects that may not be significant on a single wafer, such as micro-cluster distributions, it is difficult to identify them in single-wafer inspection. However, when such distributions repeatedly appear throughout the entire batch of wafers, it may indicate hidden process or equipment risks, such as increased particle density in specific areas due to dirt accumulation in a certain piece of equipment or wear and tear on mechanical components. Therefore, an analytical method is needed to analyze the clustering of defect distributions on the surface of wafers throughout the entire batch, identify these batch defects, and trigger production risk warnings. Summary of the Invention

[0003] This invention provides a method and apparatus for detecting defects in wafers, which can detect batch defects in wafers.

[0004] To achieve the above objectives, the technical solution adopted in the embodiments of the present invention is as follows:

[0005] A method for detecting defects in a wafer, comprising:

[0006] Obtain defect data for the surface of each wafer, including the type, size, and location coordinates of the defects;

[0007] The wafers in the same batch are partitioned according to a uniform rule. Each wafer is divided into I regions. The defect density value of each region is calculated. The defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region. The defect data of the i-th region is the sum of the defect data of all wafers in the same batch in the i-th region. i is greater than 0 and less than 1.

[0008] The defect density value of each region is compared with the defect density threshold. If the defect density value of a region is more than N times the corresponding defect density threshold, or if the defect density value of the same region of more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers.

[0009] In some embodiments, calculating the defect density value for each region includes:

[0010] The kernel density estimation method is used to calculate the defect density value of each region based on the defect data of each wafer surface.

[0011] In some embodiments, obtaining the defect density threshold includes:

[0012] Obtain defect data from historical normal batches of wafers, and use the kernel density estimation method to calculate the defect density threshold for each region based on the defect data on the surface of normal wafers.

[0013] In some embodiments, N=3, M=3.

[0014] In some embodiments, acquiring defect data for each wafer surface includes:

[0015] The defect data on the surface of each wafer is collected using a wafer surface particle inspection device;

[0016] The collected defect data is processed to eliminate noise and errors in the data collection.

[0017] In some embodiments, after the batch of wafers is determined to be defective, the method further includes:

[0018] The batch of wafers is intercepted, and a risk report for the batch is generated. The risk report includes the defect mode, production process parameters, and corresponding corrective measures for the batch of wafers.

[0019] This invention also provides a wafer defect detection device, comprising:

[0020] The acquisition module is used to acquire defect data on the surface of each wafer, the defect data including the type, size and location coordinates of the defects;

[0021] The calculation module is used to partition wafers in the same batch according to a uniform rule. Each wafer is divided into I regions and the defect density value of each region is calculated. The defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region. The defect data of the i-th region is the sum of the defect data of all wafers in the same batch in the i-th region, where i is greater than 0 and less than 1.

[0022] The processing module compares the defect density value of each region with the defect density threshold. If the defect density value of a region is more than N times the corresponding defect density threshold, or if the defect density value of the same region of more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers.

[0023] This invention also provides an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the wafer defect detection method as described above.

[0024] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the wafer defect detection method described above.

[0025] This invention also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the wafer defect detection method as described above.

[0026] The beneficial effects of this invention are:

[0027] In this embodiment, defect data of each wafer surface is acquired, the wafer is divided into regions, the defect density value of each region is calculated, and the defect density value of each region is compared with a defect density threshold. Abnormal wafers are determined based on the comparison results. This embodiment reduces the manual intervention in defect comparison and diagnosis processes, improving the accuracy and efficiency of defect identification. It enables batch-level defect risk identification, thereby identifying potential process parameter problems or equipment anomalies, reducing production losses due to failure to detect potential risks in a timely manner, preventing the spread of wafer quality problems in the production chain, and allowing for timely maintenance, improving equipment stability and product yield. By conducting risk assessment and shipment interception at the batch level, it effectively prevents batch quality accidents and avoids the large-scale outflow of defective wafers. Attached Figure Description

[0028] Figure 1 A schematic flowchart illustrating the wafer defect detection method according to an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram illustrating how a wafer is divided into multiple regions according to an embodiment of the present invention.

[0030] Figure 3 and Figure 4 A schematic diagram illustrating the clustering of defects according to an embodiment of the present invention;

[0031] Figure 5 A schematic diagram showing the structure of a wafer defect detection device according to an embodiment of the present invention;

[0032] Figure 6 This is a schematic diagram showing the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0034] This invention provides a method and apparatus for detecting defects in wafers, which can detect batch defects in wafers.

[0035] This invention provides a method for detecting defects in a wafer, such as... Figure 1 As shown, it includes:

[0036] Step 101: Obtain defect data for the surface of each wafer, the defect data including the type, size and location coordinates of the defects;

[0037] Specifically, a wafer surface particle inspection device (such as SP5 or SP7) can be used to inspect the surface of each wafer, acquiring defect data such as the location coordinates, size, and type of each defect, and generating a map of the defect distribution. In some embodiments, the acquired defect data can also be processed to eliminate noise and errors in data acquisition, thereby improving the accuracy of defect detection.

[0038] In this embodiment, defect data of all wafers in a batch (Lot) can also be imported into the data management system to summarize the defect distribution of multiple wafers in batches and create a batch defect cluster database for further cluster detection and comparative analysis.

[0039] Step 102: Divide the wafers in the same batch into regions according to a uniform rule. Each wafer is divided into I regions. Calculate the defect density value of each region. The defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region. The defect data of the i-th region is the sum of the defect data of all wafers in the same batch in the i-th region. i is greater than 0 and less than 1.

[0040] In this embodiment, wafers from the same batch can be partitioned according to a uniform rule, dividing the wafer surface into I different regions, such as... Figure 2As shown, each wafer is divided into Region 1, Region 2, Region 3, ..., Region I. For wafers in the same batch, regions with the same number have the same location and size. In some embodiments, the wafer surface can be divided into central regions, edge regions, specific corner regions, etc. After partitioning the wafer, the defect density value of each region is calculated. Specifically, the kernel density estimation method can be used to calculate the defect density value of each region to assess the concentration of defects in each region.

[0041] Step 103: Compare the defect density value of each region with the defect density threshold. If the defect density value of a region is more than N times the corresponding defect density threshold, or if the defect density value of the same region of more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers.

[0042] In this embodiment, defect data from historical normal batches of wafers can be obtained, and a kernel density estimation method is used to calculate the defect density threshold for each region based on the defect data on the surface of the normal wafers. This embodiment can establish a standard density deviation model based on the defect data from historical normal batches of wafers, including the normal defect density range and its upper and lower limits of fluctuation for different regions. Combining the defect clustering trend at the same location on adjacent wafers in the batch data, machine learning models (such as clustering algorithms and anomaly detection algorithms) are used to identify the characteristic differences between normal and anomalous distributions.

[0043] In some embodiments, N can be 3 and M can be 3. Of course, the values ​​of N and M are not limited to 3 and can be other values.

[0044] This embodiment can perform batch cluster detection on all wafers within a batch. By comparing the defect density values ​​of each region with the defect density threshold, abnormal regional clustering phenomena can be identified. In some embodiments, once the defect density value of a certain region exceeds three times the defect density threshold, the wafers in that batch are determined to be abnormal wafers. Alternatively, if the defect density value of the same region on three or more wafers in that batch is greater than the corresponding defect density threshold, the wafers in that batch are determined to be abnormal wafers. In this way, when high-density defect clustering regions repeatedly appear at the same location on multiple wafers in a batch, and the clustering characteristics of adjacent wafers are similar (such as a slight scratch in the lower right corner), a batch clustering anomaly warning can be triggered.

[0045] This embodiment can also use morphological analysis methods to identify the defect clustering distribution pattern in areas with high defect density values. It determines whether the area conforms to the characteristics of abnormal clusters (such as linear distribution or dot-like clusters). Figure 3 and Figure 4As shown in the figure, we will further analyze whether its distribution pattern has the characteristics of batch risk.

[0046] As batch data accumulates, this embodiment can also optimize and adjust the defect density threshold to make the interception rules more consistent with the actual production environment.

[0047] In this embodiment, defect data of each wafer surface is acquired, the wafer is divided into regions, the defect density value of each region is calculated, and the defect density value of each region is compared with a defect density threshold. Abnormal wafers are determined based on the comparison results. This embodiment reduces the manual intervention in defect comparison and diagnosis processes, improving the accuracy and efficiency of defect identification. It enables batch-level defect risk identification, thereby identifying potential process parameter problems or equipment anomalies, reducing production losses due to failure to detect potential risks in a timely manner, preventing the spread of wafer quality problems in the production chain, and allowing for timely maintenance, improving equipment stability and product yield. By conducting risk assessment and shipment interception at the batch level, it effectively prevents batch quality accidents and avoids the large-scale outflow of defective wafers.

[0048] In this embodiment, potential process problems or equipment malfunctions can be identified based on the batch wafer determination results. If a batch of wafers is determined to be an abnormal wafer, it indicates that there are potential process problems or equipment malfunctions in the corresponding production process parameters (such as crystal growth rate, temperature, pressure, etc.).

[0049] To improve the accuracy of the judgment, historical data can be used to perform correlation analysis on the production process parameters to determine whether the production process parameters corresponding to this batch of wafers are abnormal. After this batch of wafers is determined to be abnormal, the production process parameters of this batch of wafers can be matched with the production process parameters of the first wafer in the historical database. If the matching degree between the production process parameters of this batch of wafers and the production process parameters of the first wafer is greater than or equal to a preset matching degree, it is determined that the production process parameters of this batch of wafers are abnormal; if the matching degree between the production process parameters of this batch of wafers and the production process parameters of the first wafer is less than a preset matching degree, it is determined that the production process parameters of this batch of wafers are not abnormal.

[0050] In some embodiments, after the batch of wafers is determined to be defective, the method further includes:

[0051] The batch of wafers is intercepted, and a risk report for the batch is generated. The risk report includes the defect mode, production process parameters, and corresponding corrective measures for the batch of wafers.

[0052] In this embodiment, abnormal wafers can be intercepted in a timely manner, and detailed risk reports and improvement suggestions can be provided, thereby effectively preventing the outflow of batches of defective products. By conducting risk assessment and shipment interception at the batch level, batch quality accidents can be effectively prevented, avoiding the large-scale outflow of defective wafers. When an abnormal density clustering of defects is identified in a certain area or location of a batch of wafers, the continued flow of that batch of wafers can be automatically paused, triggering an early warning and sending a risk alert to the operators. It can also automatically generate a detailed risk report, including the clustering area, density, distribution pattern, and other characteristics of defects in each wafer in the batch, and give a risk level. This risk report is sent to the production and engineering teams for quick troubleshooting of equipment or process problems.

[0053] This invention also provides a wafer defect detection device, such as... Figure 5 As shown, it includes:

[0054] The acquisition module 21 is used to acquire defect data on the surface of each wafer, the defect data including the type, size and location coordinates of the defects;

[0055] Calculation module 22 is used to partition wafers in the same batch according to a uniform rule. Each wafer is divided into I regions and the defect density value of each region is calculated. The defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region. The defect data of the i-th region is the sum of the defect data of all wafers in the same batch in the i-th region, where i is greater than 0 and less than I.

[0056] Processing module 23 is used to compare the defect density value of each region with the defect density threshold. If the defect density value of a region is more than N times the corresponding defect density threshold, or if the defect density value of the same region of more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers.

[0057] In this embodiment, defect data of each wafer surface is acquired, the wafer is divided into regions, the defect density value of each region is calculated, and the defect density value of each region is compared with a defect density threshold. Abnormal wafers are determined based on the comparison results. This embodiment reduces the manual intervention in defect comparison and diagnosis processes, improving the accuracy and efficiency of defect identification. It enables batch-level defect risk identification, thereby identifying potential process parameter problems or equipment anomalies, reducing production losses due to failure to detect potential risks in a timely manner, preventing the spread of wafer quality problems in the production chain, and allowing for timely maintenance, improving equipment stability and product yield. By conducting risk assessment and shipment interception at the batch level, it effectively prevents batch quality accidents and avoids the large-scale outflow of defective wafers.

[0058] In some embodiments, the calculation module 22 is specifically used to calculate the defect density value of each region based on the defect data of each wafer surface using a kernel density estimation method.

[0059] In some embodiments, the calculation module 22 is also used to acquire defect data of historical normal batch wafers and use the kernel density estimation method to calculate the defect density threshold of each region based on the defect data of the normal wafer surface.

[0060] In some embodiments, N=3, M=3.

[0061] In some embodiments, the acquisition module 21 is used to collect defect data of the surface of each wafer using a wafer surface particle detection device; and to process the collected defect data to eliminate noise in the defect data and errors in data acquisition.

[0062] In some embodiments, the apparatus further includes:

[0063] The interception module is used to intercept the batch of wafers and generate a risk report for the batch of wafers. The risk report includes the defect mode, production process parameters and corresponding corrective measures for the batch of wafers.

[0064] Please refer to Figure 6 The present invention also provides an electronic device 30, including a processor 31, a memory 32, and a computer program stored in the memory 32 and executable on the processor 31. When the computer program is executed by the processor 31, it implements the various processes of the above-described wafer defect detection method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0065] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described wafer defect detection method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage, or any other non-transmission medium that can be used to store information accessible to the terminal device under test. As defined in this article, computer-readable storage media do not include transient media, such as modulated data signals and carrier waves.

[0066] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above-described... Figure 1 The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0067] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0068] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0069] In the various method embodiments of this disclosure, the sequence numbers of each step are not intended to limit the order of the steps. For those skilled in the art, any changes in the order of the steps are within the scope of protection of this disclosure without any creative effort.

[0070] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, since the embodiments are basically similar to the product embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the product embodiments.

[0071] The above description represents the preferred embodiments of this disclosure. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles described herein, and these improvements and modifications should also be considered within the scope of protection of this disclosure.

Claims

1. A method for detecting defects in a wafer, characterized in that, include: Obtain defect data for the surface of each wafer, including the type, size, and location coordinates of the defects; The wafers in the same batch are partitioned according to a uniform rule. Each wafer is divided into I regions. The defect density value of each region is calculated. The defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region. The defect data of the i-th region is the sum of the defect data of all wafers in the same batch in the i-th region. i is greater than 0 and less than 1. The defect density value of each region is compared with the defect density threshold. If the defect density value of a region is more than N times the corresponding defect density threshold, or if the defect density value of the same region of more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers. The calculation of the defect density value for each region includes: The kernel density estimation method is used to calculate the defect density value of each region based on the defect data of each wafer surface; Obtaining the defect density threshold includes: Obtain defect data from historical normal batches of wafers, and use the kernel density estimation method to calculate the defect density threshold for each region based on the defect data on the surface of each normal wafer.

2. The wafer defect detection method according to claim 1, characterized in that, N=3, M=3.

3. The wafer defect detection method according to claim 1, characterized in that, The acquisition of defect data on the surface of each wafer includes: The defect data on the surface of each wafer is collected using a wafer surface particle inspection device; The collected defect data is processed to eliminate noise and errors in the data collection.

4. The wafer defect detection method according to claim 1, characterized in that, After determining that the batch of wafers is defective, the method further includes: The batch of wafers is intercepted, and a risk report for the batch is generated. The risk report includes the defect mode, production process parameters, and corresponding corrective measures for the batch of wafers.

5. A wafer defect detection device, characterized in that, include: The acquisition module is used to acquire defect data on the surface of each wafer, the defect data including the type, size and location coordinates of the defects; The calculation module is used to partition wafers in the same batch according to a uniform rule. Each wafer is divided into I regions and the defect density value of each region is calculated. The defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region. The defect data of the i-th region is the sum of the defect data of all wafers in the same batch in the i-th region, where i is greater than 0 and less than 1. The processing module is used to compare the defect density value of each region with the defect density threshold. If the defect density value of a region is more than N times the corresponding defect density threshold, or if the defect density value of the same region of more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers. The calculation module is specifically used to calculate the defect density value of each region based on the defect data of each wafer surface using the kernel density estimation method; to obtain the defect data of historical normal batch wafers, and to calculate the defect density threshold of each region based on the defect data of each normal wafer surface using the kernel density estimation method.

6. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the wafer defect detection method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the wafer defect detection method as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the wafer defect detection method as described in any one of claims 1 to 4.

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